Semantic-Matching Loss. To promote strcutrual and semantic consistency between the layout \(S\)  and image \(X\)  produced by each of the synthesis stages, we complement the standard fidelity loss \(\mathcal {L}(D(\cdot ))\)  with two new objectives: Semantic Matching and Segment Fidelity. We note that the relation between a given layout and the set of possible images that could correspond to it is a one-to-many relation. To encourage it, we incorporate into the discriminator a U-Net [1]} to predict the segmentation \(S^{\prime }\)  of each image, compare it using cross-entropy to the source layout, and add the resulting loss term to the discriminator (over real samples) and to the generator (over fake ones).
